POPLHLTH 706 : Statistics in Health Science
Medical and Health Sciences
2025 Semester One (1253) (15 POINTS)
Course Prescription
Course Overview
The course covers aspects of essential statistics in health sciences. Students will learn how to interpret medical and epidemiological data and understand the statistics that are commonly reported in medical research reports. Practical skills in the use of the statistical analysis package R will be developed, including basic data management and cleaning, descriptive analysis and the use of common statistical tests and regression models.
Course Contacts
Capabilities Developed in this Course
Capability 3: | Knowledge and Practice |
Capability 4: | Critical Thinking |
Capability 5: | Solution Seeking |
Capability 6: | Communication |
Capability 8: | Ethics and Professionalism |
Learning Outcomes
- Develop an understanding of the rationale and requirement for, and the importance of statistical methods in health data analysis. (Capability 3, 4, 5 and 8)
- Recognise and interpret statistical methods and results that are commonly reported in health research. (Capability 3 and 4)
- Identify and apply appropriate statistical methods to summarise and analyse health data. (Capability 3, 4, 5 and 8)
- Effectively communicate health data description and analysis results. (Capability 3, 4 and 6)
- Use R statistical software to manage, summarise and analyse data. (Capability 3, 4 and 5)
Assessments
Assessment Type | Percentage | Classification |
---|---|---|
Coursework 1 - Data description using R | 15% | Individual Coursework |
Coursework 2 - Basic statistical tests using R | 15% | Individual Coursework |
Coursework 3 - Regression modelling using R | 20% | Individual Coursework |
Final Exam | 50% | Individual Coursework |
4 types | 100% |
Assessment Type | Learning Outcome Addressed | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||||||
Coursework 1 - Data description using R | ||||||||||
Coursework 2 - Basic statistical tests using R | ||||||||||
Coursework 3 - Regression modelling using R | ||||||||||
Final Exam |
Workload Expectations
This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 24 hours of lectures, 8 hours tutorial, 10 hours of reading and thinking about the content and 10 hours of work on assignments and/or test preparation.
Delivery Mode
Campus Experience
Attendance is expected at scheduled activities including tutorials.
The course will not include live online events. Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The activities for the course are scheduled as block delivery. Block teaching will take place between 9am and 2pm on 8 Wednesdays across semester 1. Each teaching day includes lectures from 9-12 followed by a tutorial in a computer lab from 1-2pm.
Teaching dates for 2025 are: 05 March, 19 March, 02 April, 09 April, 30 April, 14 May, 28 May, 04 June.
Learning Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
- Primer of biostatistics. Textbook by Stanton A Glantz.
- Medical statistics from scratch: an introduction for health professionals . Textbook by David Bowers. Full text available online via the UoA library catalogue.
- The Epidemiologist R Handbook, 2021. Online resource by Batra, Neale, et al: https://epirhandbook.com/
- Quick-R. Online resource: https://www.statmethods.net/. Linked to: R in Action - Data analysis and graphics with R. Textbook by Robert I. Kabacoff that significantly expands upon the material in the Quick-R website.
- R for Data Science. Textbook by Hadley Wickham and Garrett Grolemund. Available online: https://r4ds.had.co.nz/index.html
- R graphics. Textbook by Paul Murrell. Full text available online via the UoA library catalogue.
- The R Book. Textbook by Michael J. Crawley. Full text available online via the UoA library catalogue.
Student Feedback
At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.
Your feedback helps teachers to improve the course and its delivery for future students.
Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.
Course materials have been revised for 2025 in response to student feedback.
Other Information
This course will use the R statistical package. There will be introductory R materials available before the start of the semester, which will benefit students with no experience using this (or similar) software.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.
Class Representatives
Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Learning Continuity
In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.